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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 8
Hydrol. Earth Syst. Sci., 20, 3361–3377, 2016
https://doi.org/10.5194/hess-20-3361-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 20, 3361–3377, 2016
https://doi.org/10.5194/hess-20-3361-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 23 Aug 2016

Research article | 23 Aug 2016

Comparing the Normalized Difference Infrared Index (NDII) with root zone storage in a lumped conceptual model

Nutchanart Sriwongsitanon et al.

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Cited articles

Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, https://doi.org/10.5194/hess-12-1323-2008, 2008.
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology, Hydrol. Sci. Bull., 24, 43–69, 1979.
Ceccato, P., Flasse, S., and Grégoire, J. M.: Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2, Validations and applications, Remote Sens. Environ., 82, 198–207, https://doi.org/10.1016/S0034-4257(02)00036-6, 2002.
Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S., and Grégoire, J. M.: Detecting vegetation leaf water content using reflectance in the optical domain, Remote Sens. Environ., 77, 22–33, https://doi.org/10.1016/S0034-4257(01)00191-2, 2001.
Cheng, Y. B., Zarco-Tejada, P. J., Riaño, D., Rueda, C. A., and Ustin, S. L.: Estimating vegetation water content with hyperspectral data for different canopy scenarios: Relationships between AVIRIS and MODIS indexes, Remote Sens. Environ., 105, 354–366, https://doi.org/10.1016/j.rse.2006.07.005, 2006.
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We demonstrated that the readily available NDII remote sensing product is a very useful proxy for moisture storage in the root zone of vegetation. We compared the temporal variation of the NDII with the root zone storage in a hydrological model of eight catchments in the Upper Ping River in Thailand, yielding very good results. Having a reliable NDII product that can help us to estimate the actual moisture storage in catchments is a major contribution to prediction in ungauged basins.
We demonstrated that the readily available NDII remote sensing product is a very useful proxy...
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