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Estimation of plant water content by spectral absorption features centered at 1,450 nm and 1,940 nm regions

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Abstract

Vegetation water content could possibly provide widespread utility in agriculture, forestry and hydrology. In this article, three species leaves were measured radiometrically in order to determine a relationship between leaf water status and the spectral feature centered at 1,450 and 1,940 nm where there are strong water absorptions. The first step of our research is to measure leaf spectra with a FieldSpec-FR. After the spectral analysis using the continuum removal technique, the spectral absorption feature parameters: absorption band depth (D 1450, D 1940), the normalized band depth of absorption in 1,450 and 1,940 nm (BNA1450, BNA1940), the ratio of the two reflectance of continuum line (R 1450i /R 1940i ), the ratio of the two band depth (D 1450/D 1940) and the ratio of the two absorption areas (A 1450/A 1940) in the two wavebands were extracted from each leaf spectrum. The fuel moisture content (FMC), specific leaf weight (SLW), equivalent water thickness (EWT) were measured for each leaf sample. A correlation analysis was conducted between the spectral absorption feature parameters and corresponding FMC, SLW and EWT. In addition, some existing indices for assessing water status such as WI (water index), WI/NDVI (water index/normalized difference vegetation index), MSI (moisture stress index), NDWI (normalized difference water index)were calculated and the correlation between them and water status were analyzed too. The results by comparing the correlations indicated that the spectral absorption feature indices we proposed were better. The indexes BNA1940, D 1450/D 1940, and A 1450/A 1940 were well correlated with FMC, and the correlation between the indexes D 1450, D 1940, R 1450i /R 1940i and EWT were strong. The index A 1450/A 1940 was tested to be a good indictor for evaluating plant water content, because there was strongest positive correlation between it and FMC than other indices.

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References

  • Aber, J. D., Bolster, K. L., Newman, S. D., Soulia, M., & Martin, M. E. (1994). Analyses of forest foliage II: Measurement of carbon fraction and nitrogen content by end-member analysis. Journal of Near Infrared Spectroscopy, 2, 15–23.

    Article  CAS  Google Scholar 

  • Bauer, M. E. (1985). Spectral inputs to crop identification and condition assessment. Proceedings of the IEEE, 73, 1071–1085. doi:10.1109/PROC.1985.13238.

    Article  Google Scholar 

  • Bowman, W. D. (1989). The relationship between leaf water status, gas exchange, and spectral reflectance in cotton leaves. Remote Sensing of Environment, 30, 249–255. doi:10.1016/0034-4257(89)90066-7.

    Article  Google Scholar 

  • Carter, G. A. (1991). Primary and secondary effects of water content on the spectral reflectance of leaves. American Journal of Botany, 78(7), 916–924. doi:10.2307/2445170.

    Article  Google Scholar 

  • Ceccato, P., Gobron, N., Flasse, S., Pinty, B., & Tarantola, S. (2002). Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1, Theoretical approach. Remote Sensing of Environment, 82, 188–197. doi:10.1016/S0034-4257(02)00037-8.

    Article  Google Scholar 

  • Chuvieco, E., Cocero, D., Riaño, D., Martin, P., Martínez-Vega, J., de la Riva, J., et al. (2004). Combining NDVI and Surface Temperature for the estimation of live fuels moisture content in forest fire danger rating. Remote Sensing of Environment, 92(3), 322–331. doi:10.1016/j.rse.2004.01.019.

    Article  Google Scholar 

  • Curcio, J. A., & Petty, C. C. (1951). The near infrared absorption spectrum of liquid water. Journal of the Optical Society of America, 41, 302–304.

    Article  CAS  Google Scholar 

  • Danson, F. M., Steven, M. D., Malthus, T. J., & Clark, J. A. (1992). Highspectral resolution data for determining leaf water content. International Journal of Remote Sensing, 13(3), 461–470. doi:10.1080/01431169208904049.

    Article  Google Scholar 

  • Dawson, T. P., Curran, P. J., North, P. R. J., & Plummer, S. E. (1999). The propagation of foliar biochemical absorption features in forest canopy reflectance: A theoretical analysis. Remote Sensing of Environment, 67, 147–159. doi:10.1016/S0034-4257(98)00081-9.

    Article  Google Scholar 

  • Gao, B.-C. (1996). NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. doi:10.1016/S0034-4257(96)00067-3.

    Article  Google Scholar 

  • Gao, B.-C., & Goetz, A. F. H. (1995). Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data. Remote Sensing of Environment, 52(3), 155–162. doi:10.1016/0034-4257(95)00039-4.

    Article  Google Scholar 

  • Green, R. O., Conel, J. E., & Roberts, D. A. (1993). Estimation of aerosol optical depth and additional atmospheric parameters for the calculation of apparent reflectance from radiance measured by the airborne visible/infrared imaging spectrometer. In: Summaries of the 4th Annual JPL Airborne Geosciences Workshop, 25–29 October (Vol. 1, pp. 73–76). Washington, DC: AVIRIS Workshop. Available at: http://popo.jpl.nasa.gov/docs/workshops/93_docs/toc.html.

  • Hunt, E. R. Jr, & Rock, B. N. (1989). Detection of changes in leaf water content using near- and middle-infrared reflectance. Remote Sensing of Environment, 30, 43–54. doi:10.1016/0034-4257(89)90046-1.

    Article  Google Scholar 

  • Hunt, E. R. Jr, Rock, B. N., & Nobel, P. S. (1987). Measurement of leaf relative water content by infrared reflectance. Remote Sensing of Environment, 22, 429–435. doi:10.1016/0034-4257(87)90094-0.

    Article  Google Scholar 

  • Hunt, E. R. Jr. (1991). Airborne remote sensing of canopy water thickness scaled from leaf spectrometer data. International Journal of Remote Sensing, 12, 643–649. doi:10.1080/01431169108929679.

    Article  Google Scholar 

  • Inoue, Y., Morinaga, S., & Shibayama, M. (1993). Non-destructive estimation of water status of intact crop leaves based on spectral reflectance measurements. Nihon Sakumotsu Gakkai Kiji, 62(3), 462–469.

    CAS  Google Scholar 

  • Jacquemoud, S., Ustin, S. L., Verdebout, J., Schmuck, G., Andreoli, G., & Hosgood, B. (1996). Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sensing of Environment, 56(3), 194–202. doi:10.1016/0034-4257(95)00238-3.

    Article  Google Scholar 

  • Kokaly, R. F., & Clark, R. F. (1999a). Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67, 267–287.

    Article  Google Scholar 

  • Kokaly, R. F., & Clark, R. N. (1999b). Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67, 267–287. doi:10.1016/S0034-4257(98)00084-4.

    Article  Google Scholar 

  • Meyer, W. S., Peicosky, D. C., & Schaefer, N. L. (1985). Errors in field measurement of leaf difussive conductance associated with leaf temperature. Agricultural and Forest Meteorology, 36, 55–64. doi:10.1016/0168-1923(85)90065-6.

    Article  Google Scholar 

  • Palmer, K. F., & Williams, D. (1974). Optical properties of water in the near infrared. Journal of the Optical Society of America, 64, 1107–1110.

    Article  CAS  Google Scholar 

  • Parker, J. (1952). Desiccation in conifer leaves: Anatomical changes and determination of the lethal level. Botanical Gazette (Chicago, Ill.), 113, 189–198. doi:10.1086/335761.

    Article  Google Scholar 

  • Peñuelas, J., Filella, I., Biel, C., Serrano, L., & Savé, R. (1993). The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing, 14, 1887–1905. doi:10.1080/01431169308954010.

    Article  Google Scholar 

  • Peñuelas, J., Piñol, J., Ogaya, R., & Filella, I. (1997). Estimation of plant water concentration by the reflectance Water Index WI (R900/R970). International Journal of Remote Sensing, 18, 2869–2875. doi:10.1080/014311697217396.

    Article  Google Scholar 

  • Piñol, J., Filella, I., Ogaya, R., & Peñuelas, J. (1998). Ground based spectroradiometric estimation of live fine fuel moisture of Mediterranean plants. Agricultural and Forest Meteorology, 90, 173–186.

    Article  Google Scholar 

  • Riaño, D., Vaughan, P., Zarco-Tejada, E., & Ustin, P. J. (2005). Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content. Analysis at leaf and canopy level. IEEE Transactions on Geoscience and Remote Sensing, 43, 819–826.

    Article  Google Scholar 

  • Roberts, D. A., Brown, K., Green, R. O., Ustin, S. L., & Hinckley, T. (1998). Investigating the relationships between liquid water and leaf area in clonal Populus. In Summaries of the 7th Annual JPL Earth Science Workshop, 12–16 January, 1998, Pasadena, California (Vol. 1, pp. 335–344). JPL Publication 97–21. Available at: http://popo.jpl.nasa.gov/docs/workshops/98_docs/toc.html.

  • Roberts, D. A., Green, R. O., & Adams, J. B. (1997). Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS. Remote Sensing of Environment, 62, 223–240.

    Article  Google Scholar 

  • Rock, B. N., Vogelmann, J. E., Williams, D. L., Vogelmann, A. F., & Hoshizaki, T. (1986). Remote detection of forest damage. Bioscience, 36, 439–445.

    Article  Google Scholar 

  • Serrano, L., Ustin, S. L., Roberts, D. A., Gamon, J. A., & Peñuelas, J. (2000). Deriving water content of chaparral vegetation from AVIRIS data. Remote Sensing of Environment, 74(3), 570–581.

    Article  Google Scholar 

  • Tian, Q., Tong, Q., Pu, R., Guo, X., & Zhao, C. (2001). Spectroscopic determinations of wheat water status using 1650–1850 nm spectral absorption features. International Journal of Remote Sensing, 22, 2329–2338.

    Article  Google Scholar 

  • Tucker, C. J. (1980). Remote sensing of leaf water content in the near infrared. Remote Sensing of Environment, 10, 23–32.

    Article  Google Scholar 

  • Ustin, S. L., Darling, D., Kefauver, S., Greenberg, J., Cheng, Y.-B., & Whiting, M. L. (2004a). Remotely sensed estimates of crop water demand. In S.P.I.E. The International Symposium on Optical Science and Technology. 49th Annual Meeting, Denver, CO, 2–6 August.

  • Ustin, S. L., Jacquemoud, S., Zarco-Tejada, P. J., & Asner, G. (2004b). Remote sensing of environmental processes, state of the science and new directions. In S. L. Ustin (Ed.), Manual of Remote Sensing. Remote Sensing for Natural Resource Management and Environmental Monitoring (Vol. 4, pp. 679–730). ASPRS. New York: Wiley.

    Google Scholar 

  • Ustin, S. L., Roberts, D. A., Jacquemoud, S., Pinzón, J., Gardner, M., Scheer, G., et al. (1998). Estimating canopy water content of chaparral shrubs using optical methods. Remote Sensing of Environment, 65(3), 280–291.

    Article  Google Scholar 

  • Woolley, J. T. (1971). Reflectance and transmittance of light by leaves. Plant Physiology, 47, 656–662.

    Article  Google Scholar 

  • Zarco-Tejada, P. J., Rueda, C. A., & Ustin, S. L. (2003). Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85(1), 109–124.

    Article  Google Scholar 

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Authors and Affiliations

  1. Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, People’s Republic of China

    Jie Wang & Ruisong Xu

  2. State Key Laboratory of Estuarine and Coastal Research, Shanghai, 200062, People’s Republic of China

    Jie Wang & Shilun Yang

  3. Graduation of Chinese Academy of Sciences, Bei**g, 100039, People’s Republic of China

    Jie Wang

  4. East China Normal University, Shanghai, 200062, People’s Republic of China

    Jie Wang & Shilun Yang

  5. CAS Key Laboratory of Marginal Sea Geology, Chinese Academy of Sciences, Guangzhou, 510640, People’s Republic of China

    Ruisong Xu

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  1. Jie Wang
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  2. Ruisong Xu
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Correspondence to Jie Wang.

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Wang, J., Xu, R. & Yang, S. Estimation of plant water content by spectral absorption features centered at 1,450 nm and 1,940 nm regions. Environ Monit Assess 157, 459–469 (2009). https://doi.org/10.1007/s10661-008-0548-3

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  • DOI: https://doi.org/10.1007/s10661-008-0548-3

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