Version 1
: Received: 24 December 2016 / Approved: 2 January 2017 / Online: 2 January 2017 (10:23:30 CET)
How to cite:
Salas, E.A.L. Vegetation Water Content Prediction: Towards More Relevant Explicatory Waveband Variables. Preprints2017, 2017010001. https://doi.org/10.20944/preprints201701.0001.v1
Salas, E.A.L. Vegetation Water Content Prediction: Towards More Relevant Explicatory Waveband Variables. Preprints 2017, 2017010001. https://doi.org/10.20944/preprints201701.0001.v1
Salas, E.A.L. Vegetation Water Content Prediction: Towards More Relevant Explicatory Waveband Variables. Preprints2017, 2017010001. https://doi.org/10.20944/preprints201701.0001.v1
APA Style
Salas, E.A.L. (2017). Vegetation Water Content Prediction: Towards More Relevant Explicatory Waveband Variables. Preprints. https://doi.org/10.20944/preprints201701.0001.v1
Chicago/Turabian Style
Salas, E.A.L. 2017 "Vegetation Water Content Prediction: Towards More Relevant Explicatory Waveband Variables" Preprints. https://doi.org/10.20944/preprints201701.0001.v1
Abstract
Although the water absorption feature (WAF) at 970 nm is not very well-defined, it may be used alongside other indices to estimate the canopy water content. The individual performance of a number of existing vegetation water content (VWC) indices against the WAF is assessed using linear regression model. We developed a new Combined Vegetation Water Index (CVWI) by merging indices to boost the weak absorption feature. CVWI showed a promise in assessing the vegetation water status derived from the 970 nm absorption wavelength. CVWI was able to differentiate two groups of dataset when regressed against the absorption feature. CVWI could be seen as an easy and robust method for vegetation water content studies using hyperspectral field data.
Keywords
hyperspectral remote sensing; water absorption feature; vegetation water content; 970 nm; CVWI; vegetation water indices
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.