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Monitoring crop N status by using red edge-based indices

Published online by Cambridge University Press:  01 June 2017

J. González-Piqueras ,
H. Lopez-Corcoles ,
S. Sánchez ,
J. Villodre ,
V. Bodas ,
I. Campos ,
A. Osann  and
A. Calera
J. González-Piqueras*
Affiliation:
GIS and Remote Sensing Group. Instituto de Desarrollo Regional. Universidad de Castilla-La Mancha. Campus Universitario SN. Albacete, Spain
H. Lopez-Corcoles
Affiliation:
Instituto Técnico Agronómico Provincial (ITAP) and FUNDESCAM, Avda. Gregorio Arcos 19, 02005 Albacete, Spain
S. Sánchez
Affiliation:
GIS and Remote Sensing Group. Instituto de Desarrollo Regional. Universidad de Castilla-La Mancha. Campus Universitario SN. Albacete, Spain
J. Villodre
Affiliation:
GIS and Remote Sensing Group. Instituto de Desarrollo Regional. Universidad de Castilla-La Mancha. Campus Universitario SN. Albacete, Spain
V. Bodas
Affiliation:
Aliara Agrícola S.L. Calle Matadero 11. 45600. Talavera de La Reina (Toledo). Spain.
I. Campos
Affiliation:
GIS and Remote Sensing Group. Instituto de Desarrollo Regional. Universidad de Castilla-La Mancha. Campus Universitario SN. Albacete, Spain
A. Osann
Affiliation:
GIS and Remote Sensing Group. Instituto de Desarrollo Regional. Universidad de Castilla-La Mancha. Campus Universitario SN. Albacete, Spain
A. Calera
Affiliation:
GIS and Remote Sensing Group. Instituto de Desarrollo Regional. Universidad de Castilla-La Mancha. Campus Universitario SN. Albacete, Spain
*
† E-mail: Jose.gonzalez@uclm.es
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Abstract

Intensive agriculture has the objective to increase nutrients use efficiency. Nitrogen (N) is a key nutrient for crops and the estimations of crop N status allow adjusting the fertilization levels to crop requirements, while reducing the environmental costs and optimizing the benefits for farmers. In this work the N status of wheat in a commercial plot has been monitored, varying the N supply taking into account the variability of the soil. The N content in the cover has been monitored simultaneously by sampling at field level and by using vegetation indices based on reflectance in the red-edge band. The results of the field campaign along a crop growth cycle show that the REP, MTCI, AIVI and CCCI calculated from narrow spectral bands show good linear correlations (R2>0.93) with respect to N content (g·m−2). These indices are stable when passing to broad bands as the case of Sentinel 2 with R2>0.9.

Keywords

Nitrogenred-edgevegetation indexchlorophyll
Type
Precision Nitrogen
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 338 - 342
Copyright
© The Animal Consortium 2017 

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References

Clevers, JGPW and Gitelson, AA 2013. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and-3. International Journal of Applied Earth Observation and Geoinformation 23, 344–351.CrossRefGoogle Scholar
Dash, J and Curran, PJ 2004. The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing 25, 5403–5413.CrossRefGoogle Scholar
Fitzgerald, GJ, Rodriguez, D and 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, 318–324.CrossRefGoogle Scholar
Gitelson, AA, Vina, A, Ciganda, V, Rundquist, DC and Arkebauer, TJ 2005. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters 32, 8.CrossRefGoogle Scholar
Hatfield, JL, Gitelson, AA, Schepers, JS and Walthall, CL 2008. Application of spectral remote sensing for agronomic decisions. Agronomy Journal 100, S117–S131.CrossRefGoogle Scholar
He, L, Song, X, Feng, W, Guo, BB, Zhang, YS, Wang, YH, Wang, CY and Guo, TC 2016. Improved remote sensing of leaf nitrogen concentration in winter wheat using multi-angular hyperspectral data. Remote Sensing of Environment 174, 122–133.CrossRefGoogle Scholar
Justes, E, Mary, B, Meynard, JM, Machet, JM and Thelier-Huche, L 1994. Determination of a critical nitrogen dilution curve for winter wheat crops. Annals of Botany 74, 397–407.CrossRefGoogle Scholar
Li, F, Miao, Y, Feng, G, Yuan, F, Yue, S, Gao, X, Liu, Y, Liu, B, Ustin, SL and Chen, X 2014. Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Research 157, 111–123.CrossRefGoogle Scholar
Lemaire, G, Plénet, D and Grindlay, D 1997. Leaf N Content as an Indicator of Crop N Nutrition Status. In G. Lemaire), (Ed.) Diagnosis of the Nitrogen Status in Crops 189–199. Berlin, Heidelberg Springer Berlin Heidelberg. Germany.CrossRefGoogle Scholar
Shaver, TM, Khosla, R and Westfall, DG 2010. Evaluation of Two Ground-Based Active Crop Canopy Sensors in Maize: Growth Stage, Row Spacing, and Sensor Movement Speed. Soil Science Society of America Journal 74, 2101–2108.CrossRefGoogle Scholar