Vegetation chlorophyll content is very important for monitoring the growth and health status of vegetation. Remote sensing of chlorophyll content holds an important potential for evaluating crop growth status and diseases and insects. The both units can represent the chlorophyll content of leaves, but there are some differences between them. The former reflects the proportion of chlorophyll in the leaf components, while the latter displays the weight of chlorophyll in the unit leaf area. Combining ten representative hyperspectral vegetation indices, the effect of measurement units on estimating crop leaf chlorophyll content was studied with winter wheat and summer corn leaf chlorophyll content data and leaf reflectance data. First, the relationships between leaf chlorophyll content and the ten selected hyperspectral vegetation indices were analyzed. Second, the estimating models of leaf chlorophyll were built. Finally, the accuracies of estimating models were also assessed based on the validating data. The results show that: (i) due to chlorophyll absorption effect at visible bands, vegetation indices were highly correlated with chlorophyll content of crop leaves and it is feasible to estimate chlorophyll with vegetation index, for example, the relative errors for the estimating model based on MERIS Terrestrial Chlorophyll Index <14%; (ii) The different measurement units of leaf chlorophyll content can give rise to differences in estimating accuracy. Measurement units of mg g⁻¹ and μg cm⁻² can both effectively describe chlorophyll content of vegetation leaves. However, the leaf chlorophyll models based on the former were generally superior to the latter. For instance, the relative errors of summer corn for measurement units of mg g⁻¹ and μg cm⁻² are 7.6 and 13.6%, respectively. The reflectance signals of leaves at visible and near infrared regions contain various absorption information on leaf components, especially for pigments. Moreover, the measurement unit of mg g⁻¹ reflects the relative content of chlorophyll in the leaf components. Therefore, the unit of mg g⁻¹ is in agreement with the reflectance signals of leaves. However, the unit of μg cm⁻² only represents the absolute weight of chlorophyll in the unit leaf area. Therefore, it is recommended that the measurement unit of mg g⁻¹ should be used as far as possible when estimating chlorophyll of crop leaves or other vegetation leaves with remote sensing.

References

1. 1)
  - 1. Gong, Z.N., Zhao, Y.L., Zhao, W.J, et al: ‘Estimation model for vegetation leaf chlorophyll content based on the spectral index content’, Acta Ecol. Sin., 2014, 34, (20), pp. 5736–5745.
2. 2)
  - 14. Nevalainen, O., Hakala, T., Suomalainen, J., et al: ‘Fast and nondestructive method for leaf level chlorophyll estimation using hyperspectral LiDAR’, Agric. For. Meteorol., 2014, 198–199, pp. 250–258.
3. 3)
  - 3. Buschmann, C., Nagel, E.: ‘In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation’, Int. J. Remote Sens., 1993, 14, pp. 711–722.
4. 4)
  - 9. Vogelmann, J.E., Rock, B.N., Moss, D.M.: ‘Red edge spectral measurements from sugar maple leaves’, Int. J. Remote Sens., 1993, 14, pp. 1563–1575.
5. 5)
  - 2. Bunham-carter, G.F.: ‘Numerical procedures and computer program for fitting an inverted Gaussian model to vegetation reflectance data’, Comput. Geosci., 1988, 14, (3), pp. 339–356.
6. 6)
  - 15. Rougean, J.L., Breon, F.M.: ‘Estimating PAR absorbed by vegetation from bidirectional reflectance measurements’, Remote Sens. Environ., 1995, 51, pp. 375–384.
7. 7)
  - 13. Sonobe, R., Wang, Q.: ‘Hyperspectral indices for quantifying leaf chlorophyll concentrations performed differently with different leaf types in deciduous forests’, Ecol. Inform., 2017, 37, pp. 1–9.
8. 8)
  - 4. Pinar, A., Curran, P.J.: ‘Technical note grass chlorophyll and the reflectance red edge’, Int. J. Remote Sens., 1996, 17, (2), pp. 351–357.
9. 9)
  - 6. Daughtry, C.S.T., Walthall, C.L., Kim, M.S., et al: ‘Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance’, Remote Sens. Environ., 2000, 74, pp. 229–239.
10. 10)
  - 5. Haboudane, D., Miller, J.R., Tremblay, N., et al: ‘Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture’, Remote Sens. Environ., 2002, 81, pp. 416–426.
11. 11)
  - 11. Jacquemoud, S., Ustin, S.L., Verdebout, J., et al: ‘Estimating leaf biochemistry using the PROSPECT leaf optical properties model’, Remote Sens. Environ., 1996, 56, pp. 194–202.
12. 12)
  - 16. Wu, C., Niu, Z., Tang, Q., et al: ‘Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation’, Agric. For. Meteorol., 2008, 148, p. 1230.
13. 13)
  - 12. Dawson, T.P., Curran, P.J., Plummer, S.E.: ‘LIBERTY – modeling the effects of leaf biochemical concentration on reflectance spectra’, Remote Sens. Environ., 1998, 65, pp. 50–60.
14. 14)
  - 17. Zhang, S., Liu, L.Y., Huang, W.J.: ‘Retrieval of canopy chlorophyll density based on hyperpectral vegetation incices’, Remote Sens. Inf., 2013, 28, (3), pp. 94–101.
15. 15)
  - 10. Zarco-Tejada, P.J., Miller, J.R., Noland, T.L., et al: ‘Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data’, IEEE Trans. Geosci. Remote Sens., 2001, 39, pp. 1491–1507.
16. 16)
  - 8. Sims, D., Gamon, J.: ‘Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages’, Remote Sens. Environ., 2002, 81, pp. 337–354.
17. 17)
  - 7. Dash, J., Curran, P.J.: ‘The MERIS terrestrial chlorophyll index’, Int. J. Remote Sens., 2004, 25, pp. 5403–5413.

Effect of measure units on estimating crop LEAF chlorophyll content with remote sensing

References

Related content