Biophysical attributes estimation from satellite images in arid regions

Qi, Jiaguo; Wallace, O C

doi:10.1109/igarss.2002.1026426

Cited by 4 publications

(2 citation statements)

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“…Multispectral imagery is more widely used than hyperspectral imagery at large scales due to availability (Z. Li & Guo, 2015). Several vegetation indices that represent NPV coverage calculated from selected bands of multispectral reflectance have been developed, such as the Normalized Difference Senescent Vegetation Index (NDSVI) (Qi & Wallace, 2002), the Soil Adjusted Total Vegetation Index (SATVI) (Marsett et al., 2006), and the Dead Fuel Index (DFI) (Cao et al., 2010), but they are generally considered to be site‐specific (X. Li et al., 2016). Another technique to acquire NPV fractions is the spectral mixture analysis (SMA) which uses all bands of the reflectance spectrum (Asner & Heidebrecht, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Li & Guo, 2015). Several vegetation indices that represent NPV coverage calculated from selected bands of multispectral reflectance have been developed, such as the Normalized Difference Senescent Vegetation Index (NDSVI) (Qi & Wallace, 2002), the Soil Adjusted Total Vegetation Index (SATVI) (Marsett et al, 2006), and the Dead Fuel Index (DFI) (Cao et al, 2010), but they are generally considered to be site-specific (X. Li et al, 2016).…”

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confidence: 99%

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Effects of Non‐Photosynthetic Vegetation on Dust Emissions

Huang

Foroutan

2022

JGR Atmospheres

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Mineral dust is among the top contributors to global aerosol loads. Ability of non‐photosynthetic vegetation (NPV) to suppress dust emission has been widely acknowledged but a realistic representation of NPV has not been tested with regional‐to‐global scale models. In this study, we implemented a satellite‐based total vegetation data set, which included NPV, into a regional atmospheric chemistry model and conducted simulations for the year 2016 over the conterminous United States. To test the response of dust simulations to the NPV coverage, we conducted a control simulation incorporating only the photosynthetic vegetation (PV). Simulated dust emissions decrease by 10%–70% over most of the southwestern US from spring to autumn due to NPV. Reductions in dust concentrations are the largest in spring, which attenuate the overpredictions of fine soil concentrations, but accentuate the underpredictions in summer. Overall, the mean errors and correlations of annual simulations are slightly improved with NPV. NPV modulates dust emissions mainly by sheltering the surface and increasing the threshold velocity through drag partitioning. Moreover, we investigated the effect of vegetation height and addressed its uncertainties through a series of sensitivity tests. We observed that a 50% variation in predefined vegetation heights results in small changes in soil concentrations over majority of southwestern US, but causes up to 30% changes at local hotspots. This study highlights the significance of including NPV into the dust model and points out the importance of validation of total vegetation datasets as well as more realistic representation of vegetation heights and seasonality.

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