Skip to main content Accessibility help
×
Home

RELIABILITY OF NDVI DERIVED BY HIGH RESOLUTION SATELLITE AND UAV COMPARED TO IN-FIELD METHODS FOR THE EVALUATION OF EARLY CROP N STATUS AND GRAIN YIELD IN WHEAT

  • PAOLO BENINCASA (a1), SARA ANTOGNELLI (a1), LUCA BRUNETTI (a1), CARLO ALBERTO FABBRI (a2), ANTONIO NATALE (a2), VELIA SARTORETTI (a2), GIANLUCA MODEO (a2), MARCELLO GUIDUCCI (a1), FRANCESCO TEI (a1) and MARCO VIZZARI (a1)...

Summary

This study was aimed at comparing in-field parameters and remote sensing NDVI (normalized difference vegetation index) by both satellite (SAT) and unmanned aerial vehicle (UAV) for the assessment of early nitrogen (N) status and prediction of yield in winter wheat (Triticum aestivum L.). Six increasing N rates, i.e., 0, 40, 80, 120, 160, 200 kg N ha−1 were applied, half at tillering and half at shooting. Thus, when the crop N status was monitored between the two N applications, consecutive N treatments differentiated from each other by just 20 kg N ha−1. The following in-field and remote sensed parameters were compared as indicators of crop vegetative and N status: plant N% (w:w) concentration; crop N uptake (Nupt); ratio between transmitted and incident photosynthetically active radiation (PARt/PARi); leaf SPAD values, an indirect index for chlorophyll content; SAT and UAV derived NDVI. As reliable indicators of wheat N availability, in-field parameters were ranked as follows: PARt/PARi ≅ Nupt > SPAD ≅ N%. The PARt/PARi, Nupt and SPAD resulted quite strongly correlated to each other. At all crop stages, the NDVI was strongly correlated with PARt/PARi and Nupt. It is of relevance that NDVI correlated quite strongly to in-field parameters and grain yield at shooting, i.e., before the second N application, when the N rate can still be adjusted. The SAT and UAV NDVIs were strongly correlated to each other, which means they can be used alternatively depending on the context.

Copyright

Corresponding author

Corresponding author. Email: paolo.benincasa@unipg.it

References

Hide All
Basso, B., Ritchie, J. T., Pierce, F. J., Braga, R. P. and Jones, J. W. (2001). Spatial validation of crop models for precision agriculture. Agricultural Systems 68:97112.
Benincasa, P., Farneselli, M., Tosti, G., Bonciarelli, U., Lorenzetti, M. C. and Guiducci, M. (2016). Eleven-year results on soft and durum wheat crops grown in an organic and in a conventional low input cropping system. Italian Journal of Agronomy 11:7784.
Bonciarelli, U., Onofri, A., Benincasa, P., Farneselli, M., Guiducci, M., Pannacci, E., Tosti, G. and Tei, F. (2016). Long-term evaluation of productivity, stability and sustainability for cropping systems in Mediterranean rainfed conditions. European Journal of Agronomy 77:146155.
Bongiovanni, R. and Lowenberg-Deboer, J. (2004). Precision agriculture and sustainability. Precision Agriculture 5:359387.
Bora, G. C., Nowatzki, J. F. and Roberts, D. C. (2012). Energy savings by adopting precision agriculture in rural USA. Energy, Sustainability and Society 2:1, 22, 5.
Bouvet, M. (2014). Radiometric comparison of multispectral imagers over a pseudo-invariant calibration site using a reference radiometric model. Remote Sensing of Environment 140:141154.
Campbell, J. B. and Wayne, R. H. (2011). Introduction to Remote Sensing (5th ed). New York: The Guildford Press.
Candiago, S., Remondino, F., De Giglio, M., Dubbini, M. and Gattelli, M. (2015). Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing. 7:40264047.
Cao, Q., Miao, Y., Feng, G., Gao, X., Li, F., Liu, B., Yue, S., Cheng, S., Ustin, S. L. and Khosla, R. (2015). Active canopy sensing of winter wheat nitrogen status: An evaluation of two sensor systems. Computers and Electronics in Agriculture 112:5467.
Carlson, T. and Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment 62:241252.
Casa, R. and Castrignanò, A. (2008). Analysis of spatial relationships between soil and crop variables in a durum wheat field using a multivariate geostatistical approach. European Journal of Agronomy 28:331342.
De la Casa, A. C. and Ovando, G. G. (2013). Estimation of Wheat Area in Córdoba, Argentina, with Multitemporal NDVI Data of SPOT-Vegetation. International Journal of Geosciences 4:13551364.
Eitel, J. U. H., Vierling, L. A., Long, D. S. and Hunt, E. R. (2011). Early season remote sensing of wheat nitrogen status using a green scanning laser. Agricultural and Forest Meteorology 151:13381345.
Hadjimitsis, D. G., Clayton, C. R. I. and Retalis, A. (2009). The use of selected pseudo-invariant targets for the application of atmospheric correction in multi-temporal studies using satellite remotely sensed imagery. International Journal of Applied Earth Observation and Geoinformation 11:192200.
Hansen, P. M. and Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment 86:542553.
Hoel, B. O. and Solhaug, K. A. (1998). Effect of irradiance on chlorophyll estimation with the Minolta SPAD-502 leaf cholorophyll meter. Annals of Botany 82:389392.
Honkavaara, E., Hakala, T., Markelin, L. and Peltoniemi, J. (2014). Metrology of image processing in spectral reflectance measurement by UAV. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 40:5358.
Huete, A. R., Liu, H. Q., Batchily, K. and van Leeuwen, W. (1997). A comparsion of vegetation indices over a Global set of TM images for EO -MODIS. Remote Sensing of Environment 59:440451.
Jia, L., Yu, Z., Li, F., Gnyp, M., Koppe, W., Bareth, G., Miao, Y., Chen, X. and Zhang, F. (2012). Nitrogen Status Estimation of Winter Wheat by Using an IKONOS Satellite Image in the North China Plain. Computer and Computing Technologies in Agriculture 369:174184.
Lelong, C. C. D., Burger, P., Jubelin, G., Roux, B., Labbé, S. and Baret, F. (2008). Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors 8:35573585.
Maresma, Á., Ariza, M., Martínez, E., Lloveras, J. and Martínez-Casasnovas, J. (2016). Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service. Remote Sensing 8:973.
Monsi, M. and Saeki, T. (1953). The light factor in plant communities and its significance for dry matter production. Japanese Journal of Botany 14:2252.
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 13:1082310843.
Perry, E., Morse-Mcnabb, E., Nuttall, J., O'Leary, G. and Clark, R. (2013). Managing wheat from space: Linking MODIS NDVI and crop models for Australian dryland wheat. International Geoscience and Remote Sensing Symposium 7:32433245.
QGIS Development Team (2014). No Title. QGIS Geographic Information System. Open Source Geospatial Foundation Project. Available at: http://qgis.osgeo.org.
Quebrajo, L., Pérez-Ruiz, M., Rodriguez-Lizana, A. and Agüera, J. (2015). An Approach to Precise Nitrogen Management Using Hand-Held Crop Sensor Measurements and Winter Wheat Yield Mapping in a Mediterranean Environment. Sensors 15:55045517.
Raun, W. R., Solie, J. B., Johnson G, V.., Stone, M. L., Lukina, E. V., Thomason, W. E. and Schepers, J. S. (2001). In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal 93:131.
Ren, J., Chen, Z., Zhou, Q. and Tang, H. (2008). Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation 10:403410.
Saberioon, M. M., Amin, M. S. M., Anuar, A. R., Gholizadeh, A., Wayayok, A. and Khairunniza-Bejo, S. (2014). Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale. International Journal of Applied Earth Observation and Geoinformation 32:3545.
Sultana, S. R., Ali, A., Ahmad, A., Mubeen, M., Ahmad, S., Ercisli, S. and Jaafar, H. Z. E. (2014). Normalized Difference vegetation index as a tool for wheat yield estimation: A case study from Faisalabad, Pakistan. The Scientific World Journal 2014, ID 725326, 8 p.
Tei, F., Benincasa, P. and Guiducci, M. (2003). Critical nitrogen concentration in lettuce. Acta Horticulturae 627:227232.
Thenkabail, P. S., Smith, R. B. and De Pauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment 71:158182.
Tilling, A. K., O'Leary, G. J., Ferwerda, J. G., Jones, S. D., Fitzgerald, G. J., Rodriguez, D. and Belford, R. (2007). Remote sensing of nitrogen and water stress in wheat. Field Crops Research 104:7785.
Torres-Sánchez, J., Peña, J. M., de Castro, A. I. and López-Granados, F. (2014). Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers and Electronics in Agriculture 103:104113.
Ulissi, V., Antonucci, F., Benincasa, P., Farneselli, M., Tosti, G., Guiducci, M., Tei, F., Costa, C., Pallottino, F. and Menesatti, P. (2011). Nitrogen content estimation on tomato leaves by VIS-NIR non-destructive spectral reflectance system. Sensors 11:64116424.
Vermote, E. F., Tanre, D., Deuze, J. L., Herman, M. and Morcrette, J. J. (1997). Second simulation of the satellite signal in the solar spectrum, 6S: An overview. IEEE Transactions on Geoscience and Remote Sensing 35:675686.
Wu, B., Gommes, R., Zhang, M., Zeng, H., Yan, N., Zou, W., Zheng, Y., Zhang, N., Chang, S., Xing, Q. and van Heijden, A. (2015). Global Crop Monitoring: A Satellite-Based Hierarchical Approach. Remote Sensing 7: 39073933.
Zhang, W., Tang, L., Yang, X., Liu, L., Cao, W. and Zhu, Y. (2015). A simulation model for predicting canopy structure and light distribution in wheat. European Journal of Agronomy 67:111.
Type Description Title
WORD
Supplementary materials

Benincasa supplementary material
Benincasa supplementary material

 Word (548 KB)
548 KB

RELIABILITY OF NDVI DERIVED BY HIGH RESOLUTION SATELLITE AND UAV COMPARED TO IN-FIELD METHODS FOR THE EVALUATION OF EARLY CROP N STATUS AND GRAIN YIELD IN WHEAT

  • PAOLO BENINCASA (a1), SARA ANTOGNELLI (a1), LUCA BRUNETTI (a1), CARLO ALBERTO FABBRI (a2), ANTONIO NATALE (a2), VELIA SARTORETTI (a2), GIANLUCA MODEO (a2), MARCELLO GUIDUCCI (a1), FRANCESCO TEI (a1) and MARCO VIZZARI (a1)...

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed