Change Law of Hyperspectral Data in Related with Chlorophyll and Carotenoid in Rice at Different Developmental Stages

Abstract

Abstract:

The hyperspectral reflectances of the canopy, the first and the third unfolding leaves from the top and the panicles of two rice varieties (Xiushui 110 and Xieyou 9308) were measured by a ASD FieldSpec Pro FR^TM in field and indoor environments under three nitrogen levels at different developmental stages. The concentrations of chlorophyll and carotenoid in leaves and panicle corresponding to the spectra were determined by biochemical method. The spectral differences were significant for rice under different nitrogen levels, and the concentrations of chlorophyll and carotenoid in leaves increased along with increasing applied nitrogen. There were more pronounced differences for the pigment concentrations in rice leaves with different nitrogen levels. The spectral reflectance of canopy was gradually getting smaller in the visible region and bigger in the near infrared region as the growth stages followed before heading, but with reverse change after heading. There existed ‘blue shift’ phenomena for the position of red edge in the spectra of canopy, leaves and panicle after heading. The concentrations of chlorophyll and carotenoid in leaves presented S-shape change. The concentrations of chlorophyll and carotenoid in canopies, leaves and panicles were highly significantly correlated to the hyperspectral vegetation indices (Vis) R₉₉₀/R₅₅₃, R₁₂₀₀/R₅₅₃, R₇₅₀/R₅₅₃, R₅₅₃/R₆₇₀, R₈₀₀/R₅₅₃, PSSRa, PSNDa and the red edge positionλ_red, indicating that these VIs could be used to estimate the concentrations of chlorophyll and carotenoid in canopies, leaves and panicles of rice.

Key words: hyperspectral reflectance, chlorophyll, carotenoid, vegetation index, rice, developmental stages

TANG Yan-lin, HUANG Jing-feng, WANG Ren-chao. Change Law of Hyperspectral Data in Related with Chlorophyll and Carotenoid in Rice at Different Developmental Stages
[J]. RICE SCIENCE.

References

1 Filella D, Pen-uelas J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Intl J Remote Sens, 1994, 15(7): 1459-1470.

2 Madeira A C, Mendonca A, Ferreira M E, Freitas R L. Relationship between spectroradiometric and chlorophyll measurements in green beans. Comm Soil Sci & Plant Anal, 2000, 31(5-6): 631-643.

3 Gausman H W, Allen W A, Cardenas R, Richardson A J. Relation of light reflectance to histological and physical evaluations of cotton leaf maturity. Appl Optics, 1970, 9: 545-552.

4 Sims D A, Gamon J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ, 2002, 81: 337-354.

5 Card D H, Peterson D L, Matson P A, Aber J D. Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy. Remote Sens Environ, 1988, 26: 123-147.

6 Thomas J R, Gausman H W. Leaf reflectance vs. leaf chlorophyll and carotenoid concentrations for eight crops. Agron J, 1977, 60: 799-802.

7 Datt B. Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus leaves. Remote Sens Environ, 1998, 66: 111-121.

8 Blackburn G A. Relationships between spectral reflectance and pigment concentrations in stacks of deciduous broadleaves. Remote Sens Environ, 1999, 70: 224-237.

9 Vane G, Terrestrial imaging spectrometry: current status, future trends. Remote Sens Environ, 1993, 44(2): 109-127.

10 Blackburn G A. Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sens Environ, 1998, 66: 273-285.

11 Shibayama M, Akiyama T. Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements. Remote Sens Environ, 1991, 36: 45-53.

12 Takebe M, Yoneyama T, Inada K, Murakami T. Spectral reflectance ratio of rice canopy for estimating crop nitrogen status. Plant & Soil, 1990, 122: 295-297.

13 Wiegand C, Shibayama M, Yamagata Y, Akiyama T. Spectral observations for estimating the growth and yield of rice. Jpn J Crop Sci, 1989, 58(4): 673-683.

14 Wang R C, Chen M Z, Jiang H X. Studies on agronomic mechanism of the rice yield estimation by remote sensing: Ⅱ. The correlation analysis of agronomic parameters and spectral variables. J Zhejiang Agric Univ, 1993, 19(Suppl): 15-22. (in Chinese with English abstract)

15 Penuelas J, Baret F, Filella I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 1995, 31: 221-230.

16 Liu W D, Xiang Y Q, Zheng L F, Tong Q X, Wu C S. Relationships between rice LAI, CH.D and hyperspectra data. J Remote Sens, 2000, 4(4):279-283. (in Chinese with English abstract)

17 Wang X Z, Wang R C, Huang J F. Studies on derivative spectra and their application on agronomic parameters measurements for rice. Transactions of the CSAE, 2002, 18(1): 9-13. (in Chinese with English abstract)

18 Blackburn G A. Spectral indices for estimating photo- synthetic pigment concentrations: a test using senescent tree leaves. Intl J Remote Sens, 1998, 19(4): 657-675.

19 Gupta R K, Vijayan D, Prasad T S. New hyperspectral vegetation characterization parameters. Ads Space Res, 2001, 28(1): 201-206.

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