ASSESSMENT OF LEAF CHLOROPHYLL CONTENT IN PARTS OF THE THAR DESERT USING REMOTE SENSING TECHNIQUES
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Authors
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SURAJ KUMAR SINGH, JAGPAL SINGH TOMAR, AGNI KUNDU, SUDHANSHU and SRUTI KANGA*
Centre for Sustainable Development, Suresh GyanVihar University, Jaipur - 302025
Keywords:
Chlorophyll, NDVI, Vegetation Index, Remote SensingAbstract
Recent advances in remote sensing led to an improved approach for monitoring vegetation
properties depending on chlorophyll content. The study brings out the importance of indices as
valuable approach to measure the chlorophyll content in Luni Upper Basin (Rajasthan and Gujarat).
Chlorophyll content was measured using four different vegetation indices, i.e., modified chlorophyll
absorption ratio index, chlorophyll index green model, chlorophyll index red-edge model, and
normalized differential vegetation index. In addition, the Landsat-8, Operational Land Imager (OLI) for
2015 with spatial resolution of 30 m, and Sentinel-2 multispectral imagery of 2020 with a spatial
resolution of 10 m was used for all the three seasons Rabi, Kharif, and Zaid (2015-2020) to estimate
the vegetation cover and chlorophyll content in plant leaves. The results showed 25% area was with
good vegetation and high chlorophyll content, and 35% area was under unhealthy vegetation with
low chlorophyll content in the Kharif season. In the case of Rabi season, 15% area was under good
vegetation and 40% area was under vegetation with low chlorophyll content was recorded. In the Zaid
season, 10% area with good vegetation, and 30% area with low vegetation chlorophyll content was
estimated. The results indicated that in the arid zone, where primarily low and moderate vegetation
cover were recorded contains low and medium chlorophyll content in the plant.
References
1. Broge, N.H and Mortensen, J.V. 2002. Deriving
2. green crop area index and canopy
3. chlorophyll density of winter wheat from
4. spectral reflectance data. Remote Sensing
5. of Environment. 81: 45-57.
6. Croft, H., ARabian, J., Chen, J. M., Shang, J and
7. Liu, J. 2020. Mapping within field leaf
8. chlorophyll content in agricultural crops for
9. nitrogen management using Landsat 8
10. imagery. Precision Agriculture. 21:856-880.
11. Croft, H., Chen, J and Zhang, Y. 2014. The
12. applicability of empirical vegetation indices
13. for determining leaf chlorophyll content over
14. different leaf and canopy structures.
15. Ecological Complexity. 17: 119–130.
16. Cui, B., Zhao, Q., Huang, W., Song, X., Ye, H
17. and Zhou, X. 2019. A New integrated
18. vegetation index for the estimation of winter
19. wheat leaf chlorophyll content. Remote
20. Sensing. 11:974.
21. Cui, S and Zhou, K. 2017. A comparision of the
22. predictive potential of various vegetation
23. indices for leaf chlorophyll content. Earth
24. Science Informatics. 10:169-181.
25. Gitelson, A.A., Kaufman, Y.J., Stark, R and
26. Rundquist, D. 2002. Novel algorithms for
27. remote estimation of vegetation fraction.
28. Remote Sensing of Environment. 80:76-87.
29. Hunt Jr, E. R., Paul, C. D., James, E.M., Craig,
30. S.T.D., Eileen, M. P and Bakhyt, A. 2013. A
31. visible band index for remote sensing leaf
32. chlorophyll content at the canopy scale.
33. International Journal of Applied Earth
34. Observation and Geoinformation. 21:103–
35. Le Maire, G., Francois, CandDufrene, E. 2004.
36. Towards universal broad leaf chlorophyll
37. indices using prospect simulated database
38. and hyperspectral reflectance
39. measurements. Remote Sensing of
40. Environment. 89: 1-28 .
41. Lu, S., Lu, X., Zhao W., Liu, Yu., Wang, Z and
42. Omasa, K. 2015. Comparing vegetation
43. indices for remote chlorophyll measurement
44. of white poplar and Chinese elm leaves with
45. different adaxial and abaxial surfaces.
46. Journal of Experimental Botany. 66: 5625–
47. Mancino, G., Ferrara, A., Padula, A and Nolè, A.
48. Cross-comparision between Landsat
49. (OLI) and Landsat 7 (ETM+) derived
50. vegetation indices in a Mediterranean
51. environment. Remote Sensing. 12: 291.
52. Miller, J.R., Hare, E. W and Wu, J. 2007.
53. Quantitative characterization of the
54. vegetation red edge reflectance 1. An
55. inverted-Gaussian reflectance model.
56. International Journal of Remote Sensing. 11:
57. -1773.
58. Natalie, R., Wilson, L. M. N., Miguel, V., Leila, G.,
59. Ron, T and Andrew, S. 2016. Comparision
60. of remote sensing indices for monitoring of
61. desert cienegas. Arid Land Research and
62. Management. 30: 460-478.
63. Viña, A., Gitelson, A.A., Nguy-Robertson, A. L and
64. Peng, Yi. 2011. Comparision of different
65. vegetation indices for the remote
66. assessment of green leaf area index of
67. crops. Remote Sensing of Environment.
68. : 3468–3478.
69. XueJ and Su, B. 2017. Significant remote sensing
70. vegetation indices: A review of developments
71. and applications. Journal of Sensors. 2017:
72. -17.
73. Zhou, X., Zhang, J., Chen, D., Huang, Y., Kong,
74. W., Yuan, L., Ye, H and Huang, W. 2020
75. Assessment of Leaf Chlorophyll Content
76. Models for Winter Wheat Using Landsat-8
77. Multispectral Remote Sensing Data. Remote
78. Sensing. 12: 2574.
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