Research Article
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Year 2022, Volume: 9 Issue: 1, 170 - 178, 06.03.2022
https://doi.org/10.30897/ijegeo.958100

Abstract

References

  • Ahammad, T., Rahman, H., Dutta, S., Faisal, R., Sultana, N. (2019). Inferring Biophysical Information on Vegetation Properties Using Spectral Characterization of Remote Sensing Radiometric Ground Measurements, Dew Drop, 6, 1-10. Available at: https://drive.google.com/file/d/1Nbtx92JSrdryDnT2luctlYFMoxJvt0pX/view
  • Croft, H., Chen, J.M., Luo, X., Bartlett, P., Chen, B. & Staebler, RM. (2017) Leaf chlorophyll content as a proxy for leaf photosynthetic capacity. Global Change Biology, 23, 3513- 3524.
  • Chander, S., Gujrati, A., Hakeem, K. A. (2019). Water quality assessment of River Ganga and Chilika lagoon using AVIRIS-NG hyperspectral data, Current science, 116(7),1172-1181.
  • Curran P.J., Dungan J.L., Gholz H.L.(1990). Exploring the relationship between reflectance red edge and chlorophyll content in slash pine, Tree Physiol, 7, 33-48.
  • Chen, J.C, Yang, C.M., Wu, S.T.,Chung, Y.L.,Charles, A.L., and Chen, C.T.(2007). Leaf chlorophyll content and surface spectral reflectance of tree species along a terrain gradient in Taiwan’s Kenting National Park, Botanical Studies, 48, 71-77.
  • Dawson, T.P., Curran, P.J., and Plummer, S.E. (1998). LIBERTY -modeling the effects of leaf Biochemical concentration on reflectance spectra, Remote Sens. Environ., 65, 50-60.
  • Gamon, J.A. & Surfus, J.S. (1999) Assessing leaf pigment content and activity with a reflectometer, New Phytologist, 143, 105-117.
  • Gitelson, A.A., Gritz, Y. & Merzlyak, M.N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves, Journal of Plant Physiology, 160, 271-282.
  • Gitelson, AA, Gritz, Y, Merzlyak, MN (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for nondestructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiol.,160,271–82.
  • Huang, W., Huang, J.,Wang, X, Wang, F and Shi, J. (2013). Comparability of Red/Near-Infrared Reflectance and NDVI Based on the Spectral Response Function between MODIS and 30 Other Satellite Sensors Using Rice Canopy Spectra, Sensors, 13(12), 16023–16050.
  • Hu, H.,Zhang, G.,Zheng, k. (2014). Modeling Leaf Image, Chlorophyll Fluorescence, Reflectance From SPAD Readings, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(11), 4368-4373.
  • Hatfield, J.L. and Prueger, J.H.(2010). Value of using Different Vegetation Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices. Remote Sensing, 2, 562-78. Jacquemoud, S., Ustin, S.L., Verdebout, J., Schmuck, G., Andreoli, G., and Hosgood, B. (1996). Estimating leaf biochemistry using the PROSPECT leaf optical properties model, Remote Sens. Environ., 56, 194-202.
  • Karnieli, A.,Agam, N., Pinker,R.T. (2010). Use of NDVI and land surface temperature for drought assessment: merits and limitations, Journal of Climate, 23(3), 618–633.
  • Li, W., Sun, Z., Lu, S., Omasa, K. (2019). Estimation of the leaf chlorophyll content using multi- angular spectral reflectance factor, Plant Cell and Environment, 42(11), 3152-3165.
  • Richardson, A.D., Duigan, S.P. & Berlyn, G. (2002). An evaluation of noninvasive methods to estimate foliar chlorophyll content, New Phytologist, 153, 185-194.
  • Sims, D.A. & Gamon, J.A. (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages, Remote Sensing of Environment, 81, 337-354.
  • Trishchenko, A.P., Cihlar, J., Zhanqing, Li.(2001). Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors, Remote Sensing of Environment, 81,1-18. Weier, J., and Herring, D.(2000). Measuring Vegetation (NDVI & EVI) Available at: https://earthobservatory.nasa.gov/features/MeasuringVegetation
  • Zhang, J., Huang, W., Zhou, Q. (2014). Reflectance Variation within the In-Chlorophyll Centre Waveband for Robust Retrieval of Leaf Chlorophyll Content, PLoS ONE ,9(11), e110812. Available at: https://doi.org/10.1371/journal.pone.0110812
  • Zoran, M., Stefan, S.(2006). Climatic Changes Effects on Spectral Vegetation Indices for Forested Areas Analysis from Satellite Data, In Proceedings of the 2nd Environmental Physics Conference, Alexandria, Egypt, P.73-83.

Effect of Chlorophyll Content & Solar Irradiance on Spectral Reflectance of Vegetation Canopies Acquired By Spectro-Radiometer

Year 2022, Volume: 9 Issue: 1, 170 - 178, 06.03.2022
https://doi.org/10.30897/ijegeo.958100

Abstract

The aims of the study were: (i) to observe the effect of leaf chlorophyll content, Solar Irradiance and Normalized Difference Vegetation Index (NDVI) on spectral reflectance at Visible(Blue,Green,Red), Near Infrared (NIR) and Short Wave Infrared (SWIR) spectrum for a given number of vegetation types including Rongon (Ixora Coccinea), Hibiscus, Jhau, Grass and Togor(Tabernaemontana Divaricata).(ii) to investigate the relationship of Solar Irradiance with Normalized Difference Vegetation Index (NDVI) for the same number of vegetation types. This study used a five band hand-held spectro-radiometer “Multispectral Radiometer MSR-5” centered at wavelength 485nm, 560nm, 660nm, 830nm and 1650nm corresponding to bands 2, 3, 4, 5, 6 of Landsat 8 operational Land Imager (OLI) sensor. This spectro-radiometer provides solar irradiance and spectral reflectance values in the visible, NIR and SWIR spectrum which indirectly help to calculate Normalized Difference Vegetation Index (NDVI) for the given number of vegetation types. This study also used a Chlorophyll Meter (SPAD 502) to estimate chlorophyll concentration from the leaf of the vegetation types. The result shows that the value of the spectral reflectance correlated linearly with chlorophyll content at wavelength at 560nm and 1650 nm where the coefficient of determination R2 is 0.8761 and 0.6289 respectively. The spectral reflectance correlated inversely with NDVI at wavelength 485nm and 660nm where the coefficient of determination R2 was 0.5317 and 0.6191 respectively. This result also shows that solar irradiance relates inversely with chlorophyll content at wavelength 830nm where the coefficient of determination R2 was 0.8523.Lastly we have found that solar irradiance correlated inversely with NDVI where the coefficient of determination R2 was 0.7617.

References

  • Ahammad, T., Rahman, H., Dutta, S., Faisal, R., Sultana, N. (2019). Inferring Biophysical Information on Vegetation Properties Using Spectral Characterization of Remote Sensing Radiometric Ground Measurements, Dew Drop, 6, 1-10. Available at: https://drive.google.com/file/d/1Nbtx92JSrdryDnT2luctlYFMoxJvt0pX/view
  • Croft, H., Chen, J.M., Luo, X., Bartlett, P., Chen, B. & Staebler, RM. (2017) Leaf chlorophyll content as a proxy for leaf photosynthetic capacity. Global Change Biology, 23, 3513- 3524.
  • Chander, S., Gujrati, A., Hakeem, K. A. (2019). Water quality assessment of River Ganga and Chilika lagoon using AVIRIS-NG hyperspectral data, Current science, 116(7),1172-1181.
  • Curran P.J., Dungan J.L., Gholz H.L.(1990). Exploring the relationship between reflectance red edge and chlorophyll content in slash pine, Tree Physiol, 7, 33-48.
  • Chen, J.C, Yang, C.M., Wu, S.T.,Chung, Y.L.,Charles, A.L., and Chen, C.T.(2007). Leaf chlorophyll content and surface spectral reflectance of tree species along a terrain gradient in Taiwan’s Kenting National Park, Botanical Studies, 48, 71-77.
  • Dawson, T.P., Curran, P.J., and Plummer, S.E. (1998). LIBERTY -modeling the effects of leaf Biochemical concentration on reflectance spectra, Remote Sens. Environ., 65, 50-60.
  • Gamon, J.A. & Surfus, J.S. (1999) Assessing leaf pigment content and activity with a reflectometer, New Phytologist, 143, 105-117.
  • Gitelson, A.A., Gritz, Y. & Merzlyak, M.N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves, Journal of Plant Physiology, 160, 271-282.
  • Gitelson, AA, Gritz, Y, Merzlyak, MN (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for nondestructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiol.,160,271–82.
  • Huang, W., Huang, J.,Wang, X, Wang, F and Shi, J. (2013). Comparability of Red/Near-Infrared Reflectance and NDVI Based on the Spectral Response Function between MODIS and 30 Other Satellite Sensors Using Rice Canopy Spectra, Sensors, 13(12), 16023–16050.
  • Hu, H.,Zhang, G.,Zheng, k. (2014). Modeling Leaf Image, Chlorophyll Fluorescence, Reflectance From SPAD Readings, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(11), 4368-4373.
  • Hatfield, J.L. and Prueger, J.H.(2010). Value of using Different Vegetation Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices. Remote Sensing, 2, 562-78. Jacquemoud, S., Ustin, S.L., Verdebout, J., Schmuck, G., Andreoli, G., and Hosgood, B. (1996). Estimating leaf biochemistry using the PROSPECT leaf optical properties model, Remote Sens. Environ., 56, 194-202.
  • Karnieli, A.,Agam, N., Pinker,R.T. (2010). Use of NDVI and land surface temperature for drought assessment: merits and limitations, Journal of Climate, 23(3), 618–633.
  • Li, W., Sun, Z., Lu, S., Omasa, K. (2019). Estimation of the leaf chlorophyll content using multi- angular spectral reflectance factor, Plant Cell and Environment, 42(11), 3152-3165.
  • Richardson, A.D., Duigan, S.P. & Berlyn, G. (2002). An evaluation of noninvasive methods to estimate foliar chlorophyll content, New Phytologist, 153, 185-194.
  • Sims, D.A. & Gamon, J.A. (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages, Remote Sensing of Environment, 81, 337-354.
  • Trishchenko, A.P., Cihlar, J., Zhanqing, Li.(2001). Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors, Remote Sensing of Environment, 81,1-18. Weier, J., and Herring, D.(2000). Measuring Vegetation (NDVI & EVI) Available at: https://earthobservatory.nasa.gov/features/MeasuringVegetation
  • Zhang, J., Huang, W., Zhou, Q. (2014). Reflectance Variation within the In-Chlorophyll Centre Waveband for Robust Retrieval of Leaf Chlorophyll Content, PLoS ONE ,9(11), e110812. Available at: https://doi.org/10.1371/journal.pone.0110812
  • Zoran, M., Stefan, S.(2006). Climatic Changes Effects on Spectral Vegetation Indices for Forested Areas Analysis from Satellite Data, In Proceedings of the 2nd Environmental Physics Conference, Alexandria, Egypt, P.73-83.
There are 19 citations in total.

Details

Primary Language English
Subjects Photogrammetry and Remote Sensing
Journal Section Research Articles
Authors

Tofayel Ahammad 0000-0003-3597-3673

Publication Date March 6, 2022
Published in Issue Year 2022 Volume: 9 Issue: 1

Cite

APA Ahammad, T. (2022). Effect of Chlorophyll Content & Solar Irradiance on Spectral Reflectance of Vegetation Canopies Acquired By Spectro-Radiometer. International Journal of Environment and Geoinformatics, 9(1), 170-178. https://doi.org/10.30897/ijegeo.958100