Unmanned aerial vehicles (UAVs) present themselves as an alternative to overcome the limitations of satellite sensors in monitoring agricultural crops, motivating many studies with UAVs. They can carry sensors, which need studies for better understanding. The present study aimed to vicariously calibrate a Red-Green-Near infrared (RG-NIR) low-cost sensor on board a UAV, and to develop predictive models of biophysical parameters for a maize crop. To achieve this purpose, 15 sets of images were captured over 61 days after emergence (DAE) of the maize crop plantation. Each set of images was mosaicked and had their digital numbers (DN) converted to reflectance. After calibration, normalized difference vegetation index (NDVI) and cumulative NDVI (cNDVI) were calculated to serve as an independent variable in the models for estimating crop parameters. In the field, 54 plants were collected and evaluated for height, leaf area and dry biomass. It was observed that the NIR band had an influence on the red band, but this influence was attenuated with the empirical line calibration. NDVI was able to detect seasonal and spatial variations in maize. The NDVI model obtained on the collection day to estimate the total dry above ground biomass had better results, generating RMSE of 68.68 g m−2 and R2 of 0.81, in comparison with cNDVI. For productivity, the result was satisfactory with cNDVI, showing RMSE of 134.00 g m−2 and R2 of 0.63. Calibration of the sensor was shown to be important to attenuate influence between bands.
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Alvares, C. A., Stape, J. L., Sentelhas, P. C., De Moraes Gonçalves, J. L., & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22(6), 711–728. https://doi.org/10.1127/0941-2948/2013/0507.
Askew, S. D., Askew, W. B., & Goatley, J. M. (2019). Fineleaf fescue species and variety tolerance to glyphosate. Weed Technology, 33(1), 185–191. https://doi.org/10.1017/wet.2018.65.
Cao, S., Danielson, B., Clare, S., Koenig, S., Campos-Vargas, C., & Sanchez-Azofeifa, A. (2019). Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocols. ISPRS Journal of Photogrammetry and Remote Sensing, 149(January), 132–145. https://doi.org/10.1016/j.isprsjprs.2019.01.016.
Cheng, T., Song, R., Li, D., Zhou, K., Zheng, H., Yao, X., et al. (2017). Spectroscopic estimation of biomass in canopy components of paddy rice using dry matter and chlorophyll indices. Remote Sensing, 9(4), 319. https://doi.org/10.3390/rs9040319.
Congedo, L. (2016). Semi-Automatic Classification Plugin Documentation Release 22.214.171.124. Release, 4(0.1), 29. https://doi.org/10.13140/RG.2.2.29474.02242/1
Del Pozo, S., Rodríguez-Gonzálvez, P., Hernández-López, D., & Felipe-García, B. (2014). Vicarious radiometric calibration of a multispectral camera on board an unmanned aerial system. Remote Sensing, 6(3), 1918–1937. https://doi.org/10.3390/rs6031918.
Deng, L., Hao, X., Mao, Z., Yan, Y., Sun, J., & Zhang, A. (2018). A subband radiometric calibration method for uav-based multispectral remote sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(8), 2869–2880. https://doi.org/10.1109/JSTARS.2018.2842466.
Dianguirard, M., & Slater, P. N. (1999). Calibration of space-multispectral imaging sensors: A review. Remote Sensing of Environment, 64, 387–397. https://doi.org/10.1016/S0034-4257(98)00111-4.
Gege, P., Fries, J., Haschberger, P., Schötz, P., Schwarzer, H., Strobl, P., et al. (2009). W. Calibration facility for airborne imaging spectrometers. ISPRS Journal of Photogrammetry and Remote Sensing, 64, 387–397. https://doi.org/10.1016/j.isprsjprs.2009.01.006.
Gómez-Candón, D., De Castro, A. I., & López-Granados, F. (2014). Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, 15(1), 44–56. https://doi.org/10.1007/s11119-013-9335-4.
Gowravaram, S., Tian, P., Flanagan, H., Goyer, J., & Chao, H. (2018). UAS-based multispectral remote sensing and NDVI calculation for post disaster assessment. Proceedings of the 2018 International Conference on Unmanned Aircraft Systems, (pp. 684–691): IEEE: New York, NY, USA. https://doi.org/10.1109/ICUAS.2018.8453314.
Guo, Y., Senthilnath, J., Wu, W., Zhang, X., Zeng, Z., & Huang, H. (2019). Radiometric calibration for multispectral camera of different imaging conditions mounted on a UAV platform. Sustainability (Switzerland), 11(4), 1–24. https://doi.org/10.3390/su11040978.
Hadjimitsis, D. G., Clayton, C. R. I., & Hope, V. S. (2004). An assessment of the effectiveness of atmospheric correction algorithms through the remote sensing of some reservoirs. International Journal of Remote Sensing, 25(18), 3651–3674. https://doi.org/10.1080/01431160310001647993.
Herrero-Huerta, M., González-Aguilera, D., Rodriguez-Gonzalvez, P., & Hernández-López, D. (2015). Vineyard yield estimation by automatic 3D bunch modelling in field conditions. Computers and Electronics in Agriculture, 110, 17–26. https://doi.org/10.1016/j.compag.2014.10.003.
Hijmans, R. J., et al. (2020). Geographic data analysis and modeling. Retrieved August 25, 2019 from https://cran.r-project.org/package=raster
Honkavaara, E., Arbiol, R., Markelin, L., Martinez, L., Cramer, M., Bovet, S., et al. (2009). Digital airborne photogrammetry—A new tool for quantitative remote sensing?—A state-of-the-art review on radiometric aspects of digital photogrammetric images. Remote Sensing, 1(3), 577–605. https://doi.org/10.3390/rs1030577.
Huang, Y., Reddy, K. N., Thomson, S. J., & Yao, H. (2015). Assessment of soybean injury from glyphosate using airborne multispectral remote sensing. Pest Management Science, 71(4), 545–552. https://doi.org/10.1002/ps.3839.
Iqbal, F., Lucieer, A., & Barry, K. (2018). Simplified radiometric calibration for UAS-mounted multispectral sensor. European Journal of Remote Sensing, 51(1), 301–313. https://doi.org/10.1080/22797254.2018.1432293.
Jensen, J. R. (2015). Introductory Digital Image Processing: A Remote Sensing Perspective. Glenview, USA: Pearson Education.
Kelcey, J., & Lucieer, A. (2012). Sensor correction and radiometric calibration of a 6-band multispectral imaging sensor for UAV remote sensing. ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39(September), 393–398. https://doi.org/10.5194/isprsarchives-XXXIX-B1-393-2012.
Kross, A., McNairn, H., Lapen, D., Sunohara, M., & Champagne, C. (2015). Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. International Journal of Applied Earth Observation and Geoinformation, 34(1), 235–248. https://doi.org/10.1016/j.jag.2014.08.002.
Lai, Y. R., Pringle, M. J., Kopittke, P. M., Menzies, N. W., Orton, T. G., & Dang, Y. P. (2018). An empirical model for prediction of wheat yield, using time-integrated Landsat NDVI. International Journal of Applied Earth Observation and Geoinformation, 72(July), 99–108. https://doi.org/10.1016/j.jag.2018.07.013.
Le Maire, G., François, C., & Dufrêne, E. (2004). Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment, 89(1), 1–28. https://doi.org/10.1016/j.rse.2003.09.004.
Li, H., Zhang, H., Zhang, B., Chen, Z., Yang, M., & Zhang, Y. (2015). A method suitable for vicarious calibration of a UAV hyperspectral remote sensor. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), 3209–3223. https://doi.org/10.1109/JSTARS.2015.2416213.
Ling, B., Goodin, D. G., Raynor, E. J., & Joern, A. (2019). Hyperspectral analysis of leaf pigments and nutritional elements in tallgrass prairie vegetation. Frontiers in Plant Science, 10, 142. https://doi.org/10.3389/fpls.2019.00142.
Maechler, M. (2017). sfsmisc: Utilities from seminar fuer Statistik ETH Zurich. Retrieved Oct 25, 2019 from https://cran.r-project.org/web/packages/sfsmisc/index.html.
Maes, W. H., & Steppe, K. (2019). Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends in Plant Science, 24(2), 152–164. https://doi.org/10.1016/j.tplants.2018.11.007.
Manfreda, S., McCabe, M. F., Miller, P. E., Lucas, R., Madrigal, V. P., Mallinis, G., et al. (2018). On the use of unmanned aerial systems for environmental monitoring. Remote Sensing, 10(4), 641. https://doi.org/10.3390/rs10040641.
Mapir. (2020). Retrieved December 16, 2020, from https://www.mapir.camera/products/survey3w-camera-red-green-nir-rgn-ndvi.
Markelin, L., Honkavaara, E., Peltoniemi, J., Ahokas, E., Kuittinen, R., Hyyppä, J., et al. (2008). Radiometric calibration and characterization of large-format digital photogrammetric sensors in a test field. Photogrammetric Engineering & Remote Sensing, 74(12), 1487–1500. https://doi.org/10.14358/PERS.74.12.1487.
Moran, M., Bryant, R., Thome, K., Ni, W., Nouvellon, Y., Gonzalez-Dugo, M., et al. (2001). A refined empirical line approach for reflectance factor retrieval from Landsat-5 TM and Landsat-7 ETM+. Remote Sensing of Environment, 78(1–2), 71–82. https://doi.org/10.1016/S0034-4257(01)00250-4.
Nijland, W., de Jong, R., de Jong, S. M., Wulder, M. A., Bater, C. W., & Coops, N. C. (2014). Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras. Agricultural and Forest Meteorology, 184, 98–106. https://doi.org/10.1016/j.agrformet.2013.09.007.
Ponzoni, F. J., Shimabukuro, Y. E., & Kuplich, T. M. (2012). Sensoriamento remoto da vegetação (Remote sensing of vegetation). São Paulo, Brazil: Oficina de Texto.
Pringle, M. J. (2013). Robust prediction of time-integrated NDVI. International Journal of Remote Sensing, 34(13), 4791–4811. https://doi.org/10.1080/01431161.2013.782117.
QGIS Development Team. (2016). QGIS geographic information system. Open Source Geospatial Foundation Project, Versão. https://www.qgis.org.
R Core Team. (2019). R: A Language and Environment for Statistical Computing, Version 3.3. 1. Vienna, Austria: R Foundation for Statistical Computing; 2016. https://www.r-project.org/.
Rabatel, G., Gorretta, N., & Labbé, S. (2014). Getting simultaneous red and near-infrared band data from a single digital camera for plant monitoring applications: Theoretical and practical study. Biosystems Engineering, 117(1), 2–14. https://doi.org/10.1016/j.biosystemseng.2013.06.008.
Ren, H., Zhou, G., & Zhang, F. (2018). Using negative soil adjustment factor in soil-adjusted vegetation index (SAVI) for aboveground living biomass estimation in arid grasslands. Remote Sensing of Environment, 209(79), 439–445. https://doi.org/10.1016/j.rse.2018.02.068.
Romero, M., Luo, Y., Su, B., & Fuentes, S. (2018). Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management. Computers and Electronics in Agriculture, 147, 109–117. https://doi.org/10.1016/j.compag.2018.02.013.
Rouse, R. W. H., Haas, J. A. W., & Deering, D. W. (1974). Monitoring vegetation systems in the great plains with ERTS. Third Earth Resources Technology Satellite-1 Symposium—Volume I: Technical Presentations. NASA SP-351 (pp. 309–317). https://ntrs.nasa.gov/search.jsp?R=19740022614.
Russelle, M. P., Wilhelm, W. W., Olson, R. A., & Power, J. F. (1984). Growth analysis based on degree days. Crop Science, 24(1), 28–32. https://doi.org/10.2135/cropsci1984.0011183X002400010007x.
Staben, G. W., Pfitzner, K., Bartolo, R., & Lucieer, A. (2014). Empirical line calibration of WorldView-2 satellite imagery to reflectance data: Using quadratic prediction equations. Remote Sensing Letters, 3(6), 521–530. https://doi.org/10.1080/01431161.2011.609187.
Toureiro, C., Serralheiro, R., Shahidian, S., & Sousa, A. (2017). Irrigation management with remote sensing: Evaluating irrigation requirement for maize under Mediterranean climate condition. Agricultural Water Management, 184, 211–220. https://doi.org/10.1016/j.agwat.2016.02.010.
Wang, C., & Myint, S. W. (2015). A simplified empirical line method of radiometric calibration for small unmanned aircraft systems-based remote sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5), 1876–1885. https://doi.org/10.1109/JSTARS.2015.2422716.
Yang, G., Liu, J., Zhao, C., Li, Z., Huang, Y., Yu, H., et al. (2017). Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives. Frontiers in Plant Science, 8(June), 1111. https://doi.org/10.3389/fpls.2017.01111.
Zhang, L., Niu, Y., Zhang, H., Han, W., Li, G., Tang, J., et al. (2019). Maize canopy temperature extracted from UAV thermal and RGB imagery and its application in water stress monitoring. Frontiers in Plant Science, 10(October), 1–18. https://doi.org/10.3389/fpls.2019.01270.
Zhang, Y., Chen, D., Wang, S., & Tian, L. (2018). A promising trend for field information collection: An air-ground multi-sensor monitoring system. Information Processing in Agriculture, 5(2), 224–233. https://doi.org/10.1016/j.inpa.2018.02.002.
Zhao, D., Reddy, K. R., Kakani, V. G., Read, J. J., & Koti, S. (2005). Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton. Agronomy Journal, 97(1), 89–98. https://doi.org/10.2134/agronj2005.0089.
Zheng, B., Myint, S. W., Thenkabail, P. S., & Aggarwal, R. M. (2015). A support vector machine to identify irrigated crop types using time-series Landsat NDVI data. International Journal of Applied Earth Observation and Geoinformation, 34(1), 103–112. https://doi.org/10.1016/j.jag.2014.07.002.
Zheng, H., Cheng, T., Li, D., Zhou, X., Yao, X., Tian, Y., et al. (2018). Evaluation of RGB, color-infrared and multispectral images acquired from unmanned aerial systems for the estimation of nitrogen accumulation in rice. Remote Sensing, 10(6), 1–17. https://doi.org/10.3390/rs10060824.
Zheng, H., Ying, H., Yin, Y., Wang, Y., He, G., Bian, Q., et al. (2019). Irrigation leads to greater maize yield at higher water productivity and lower environmental costs: A global meta-analysis. Agriculture, Ecosystems and Environment, 273, 62–69. https://doi.org/10.1016/j.agee.2018.12.009.
Zhou, G., & Yin, X. (2018). Assessing nitrogen nutritional status, biomass and yield of cotton with MDVI, spad and petiole sap nitrate concentration. Experimental Agriculture, 54(4), 531–548. https://doi.org/10.1017/S0014479717000229.
Zhou, X., Zheng, H. B., Xu, X. Q., He, J. Y., Ge, X. K., Yao, X., et al. (2017). Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 246–255. https://doi.org/10.1016/j.isprsjprs.2017.05.003.
We thank the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), finance code: 001, the Department of Agricultural Engineering (DEA), the Reference Center in Water Resources (CRRH) and the Group of Studies and Solutions for Irrigated Agriculture (GESAI) of the Federal University of Viçosa for financing and supporting this study.
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dos Santos, R.A., Filgueiras, R., Mantovani, E.C. et al. Surface reflectance calculation and predictive models of biophysical parameters of maize crop from RG-NIR sensor on board a UAV. Precision Agric (2021). https://doi.org/10.1007/s11119-021-09795-x
- Remote sensing
- Vicarious calibration
- Empirical line method