Current Science

Open Access Open Access  Restricted Access Subscription Access

Comparison of Parametric and Non-Parametric Methods for Chlorophyll Estimation based on High-Resolution UAV Imagery


Affiliations
1 CSIR-Central Scientific Instruments Organization, Chandigarh 160 030, India
2 Academy of Scientific and Innovative Research, Ghaziabad 201 002, India
3 National Institute of Technical Teachers, Training and Research, Chandigarh 160 019, India
4 North East Space Application Centre, Barapani 793 103, India
5 ICAR Research Complex for NEH Region, Umiam 793 103, India
 

The present study provides a systematic comparison of parametric and non-parametric retrieval methods using high-resolution data provided by the unmanned aerial vehicle (UAV). We used turmeric crop reflectance data to evaluate the vegetation index (VI)-based parametric methods and compared them with linear and nonlinear non-parametric methods to build a rigorous LCC estimation model. The study demonstrates that the best-performing VI was the normalized green red difference index (GNRDI), with R2 = 0.68, RMSE = 0.13 and high processing speed of 0.08 s. With regard to non-parametric methods, almost all methods outperformed their parametric counterparts. Particularly, methods such as random forest (RF) and kernel ridge regression (KRR) showed the best performance characterized by R2 > 0.72 and RMSE ≤ 0.12 mg/g of fresh leaf weight. These nonparametric methods possessed the benefit of total spectral information utilization and enabled robust, non-linear relationship between the predictor and target variables, but computational complexity is a major drawback.

Keywords

Chlorophyll, Machine Learning, Unmanned Aerial Vehicle, Vegetation Index.
User
Notifications
Font Size

  • Kanning, M., Kühling, I., Trautz, D. and Jarmer, T., Highresolution UAV-based hyperspectral imagery for LAI and chlorophyll estimations from wheat for yield prediction. Remote Sensing, 2018, 10, 2000.

  • Pearson, R. L. and Miller, L. D., Remote mapping of standing crop biomass for estimation of the productivity of the short grass prairie. Remote Sensing Environ., 1972, VIII, 1355.

  • Pérez-Patricio, M. et al., Optical method for estimating the chlorophyll contents in plant leaves. Sensors, 2018, 18, 650.

  • Thomas, J. and Gausman, H., Leaf reflectance versus leaf chlorophyll and carotenoid concentrations for eight crops 1. Agron. J., 1977, 69, 799–802.

  • Aasen, H., Burkart, A., Bolten, A. and Bareth, G., Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: from camera calibration to quality assurance. ISPRS J. Photogramm., 2015, 108, 245–259.

  • Broge, N. H. and Leblanc, E., Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing Environ., 2001, 76, 156–172.

  • Haboudane, D., Tremblay, N., Miller, J. R. and Vigneault, P., Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEET Geosci. Remote Sensing, 2008, 46, 423–437.

  • Hunt, E. R., Daughtry, C., Eitel, J. U. and Long, D. S., Remote sensing leaf chlorophyll content using a visible band index. Agron. J., 2011, 103, 1090–1099.

  • Baret, F. and Guyot, G., Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing Environ., 1991, 35, 161–173.

  • Baret, F., Guyot, G. and Major, D., TSAVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation. In 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium, Vancouver, Canada, 1989, pp. 1355–1358.

  • Bannari, A., Morin, D., Bonn, F. and Huete, A., A review of vegetation indices. Remote Sensing Rev., 1995, 13, 95–120.

  • Caicedo, J. P. R., Verrelst, J., Muñoz-Marí, J., Moreno, J. and Camps-Valls, G., Toward a semiautomatic machine learning retrieval of biophysical parameters. IEEE J. Selected Top. Appl. Earth Observ. Remote Sensing, 2014, 7, 1249–1259.

  • Hunt, E. R., Hively, W. D., Fujikawa, S., Linden, D., Daughtry, C. S. and McCarty, G., Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing, 2010, 2, 290–305.

  • Roosjen, P. P., Brede, B., Suomalainen, J. M., Bartholomeus, H. M., Kooistra, L. and Clevers, J. G., Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data–potential of unmanned aerial vehicle imagery. Int. J. Appl. Earth Observ. Geoinform., 2018, 66, 14–26.

  • Simic Milas, A., Romanko, M., Reil, P., Abeysinghe, T. and Marambe, A., The importance of leaf area index in mapping chlorophyll content of corn under different agricultural treatments using UAV images. Int. J. Remote Sensing, 2018, 39, 5415–5431.

  • Singhal, G., Bansod, B., Mathew, L., Goswami, J., Choudhury, B. and Raju, P., Estimation of leaf chlorophyll concentration in turmeric (Curcuma longa) using high-resolution unmanned aerial vehicle imagery based on Kernel Ridge Regression. J. Indian Soc. Remote Sensing, 2019, 47, 1–12.

  • Rouse Jr, J. W., Haas, R., Schell, J. and Deering, D., Monitoring vegetation systems in the Great Plains with ERTS. In NASA, 3d ERTS-1 Symposium, Goddard Space Flight Center, 1974, vol. 1, Sect. A, pp. 309–317.

  • Tucker, C. J., Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing Environ., 1979, 8, 127–150.

  • Gitelson, A. and Merzlyak, M. N., Quantitative estimation of chlorophylla using reflectance spectra: experiments with autumn chestnut and maple leaves. J. Photochem. Photobiol. Biol., 1994, 22, 247–252.

  • Hunt Jr, E. R., Doraiswamy, P. C., McMurtrey, J. E., Daughtry, C. S., Perry, E. M. and Akhmedov, B., A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int. J. Appl. Earth Observ. Geoinf., 2013, 21, 103–112.

  • Huete, A. R., A soil-adjusted vegetation index (SAVI). Remote Sensing Environ., 1988, 25, 295–309.

  • Gitelson, A. A., Gritz, Y. and Merzlyak, M. N., Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol., 2003, 160, 271–282.

  • Daughtry, C., Walthall, C., Kim, M., De Colstoun, E. B. and McMurtrey Iii, J., Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing Environ., 2000, 74, 229–239.

  • Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J. and Dextraze, L., Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing Environ., 2002, 81, 416–426.

  • Verrelst, J. et al., Quantifying vegetation biophysical variables from imaging spectroscopy data: a review on retrieval methods. Surveys Geophys., 2018, 40(3), 1–41.

  • Tipping, M. E. (ed.), Bayesian inference: An introduction to principles and practice in machine learning. In Advanced Lectures on Machine Learning, Springer, 2004, pp. 41–62.

  • Cawley, G. C., Talbot, N. L. and Chapelle, O., Estimating predictive variances with kernel ridge regression. In Machine Learning Challenges Workshop, 2005, pp. 56–77.

  • Saunders, C., Gammerman, A. and Vovk, V., Ridge regression learning algorithm in dual variables. In ICML ’98 Proceeding of the Fifteenth International Conference on Machine Learning, 1998, pp. 515–521.

  • Belgiu, M. and Drăguţ, L., Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. Remote Sensing, 2016, 114, 24–31.

  • Walton, J. T., Subpixel urban land cover estimation. Photogramm. Eng. Remote Sensing, 2008, 74, 1213–1222.

  • Gitelson, A. A. and Merzlyak, M. N., Remote sensing of chlorophyll concentration in higher plant leaves. Adv. Space Res., 1998, 22, 689–692.

  • Jones, C. L., Weckler, P., Maness, N. O., Jayasekara, R., Stone, M. and Chrz, D., Remote sensing to estimate chlorophyll concentration in spinach using multi-spectral plant reflectance. Trans. ASABE, 2007, 50, 2267–2273.

  • Hunt, E. R., Cavigelli, M., Daughtry, C. S., Mcmurtrey, J. E. and Walthall, C. L., Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precis. Agric., 2005, 6, 359–378.

  • Peng, Y., Huang, H., Wang, W., Wu, J. and Wang, X., Rapid detection of chlorophyll content in corn leaves by using least squares-support vector machines and hyperspectral images. J. Jiangsu Univ.-Natural Sci. Edn, 2011, 32, 125–174.

  • Verrelst, J., Rivera, J. P., Gitelson, A., Delegido, J., Moreno, J. and Camps-Valls, G., Spectral band selection for vegetation properties retrieval using Gaussian processes regression. Int. J. Appl. Earth Observ. Geoinform., 2016, 52, 554–567.


Abstract Views: 438

PDF Views: 138




  • Comparison of Parametric and Non-Parametric Methods for Chlorophyll Estimation based on High-Resolution UAV Imagery

Abstract Views: 438  |  PDF Views: 138

Authors

Gaurav Singhal
CSIR-Central Scientific Instruments Organization, Chandigarh 160 030, India
Babankumar Bansod
Academy of Scientific and Innovative Research, Ghaziabad 201 002, India
Lini Mathew
National Institute of Technical Teachers, Training and Research, Chandigarh 160 019, India
Jonali Goswami
North East Space Application Centre, Barapani 793 103, India
B. U. Choudhury
ICAR Research Complex for NEH Region, Umiam 793 103, India
P. L. N. Raju
North East Space Application Centre, Barapani 793 103, India

Abstract


The present study provides a systematic comparison of parametric and non-parametric retrieval methods using high-resolution data provided by the unmanned aerial vehicle (UAV). We used turmeric crop reflectance data to evaluate the vegetation index (VI)-based parametric methods and compared them with linear and nonlinear non-parametric methods to build a rigorous LCC estimation model. The study demonstrates that the best-performing VI was the normalized green red difference index (GNRDI), with R2 = 0.68, RMSE = 0.13 and high processing speed of 0.08 s. With regard to non-parametric methods, almost all methods outperformed their parametric counterparts. Particularly, methods such as random forest (RF) and kernel ridge regression (KRR) showed the best performance characterized by R2 > 0.72 and RMSE ≤ 0.12 mg/g of fresh leaf weight. These nonparametric methods possessed the benefit of total spectral information utilization and enabled robust, non-linear relationship between the predictor and target variables, but computational complexity is a major drawback.

Keywords


Chlorophyll, Machine Learning, Unmanned Aerial Vehicle, Vegetation Index.

References





DOI: https://doi.org/10.18520/cs%2Fv117%2Fi11%2F1874-1879