Applications of suits spectral model to wheat

Chance, Joseph E.

doi:10.1016/0034-4257(77)90012-8

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…We acknowledge that use of hyperspectral imagery to detect plant stress caused by larval O. nubilalis in larger cornÞelds may be more difÞcult given the inherent variability of production sized Þelds especially if there are in-Þeld differences in soil type (Chance 1977, Curran 1985. However, to our knowledge, high spectral and spatial resolution airborne hyperspectral imagery has not been previously used to detect plant stress caused by insect feeding in production corn Þelds.…”

Section: Discussionmentioning

confidence: 91%

Use of Spectral Vegetation Indices Derived from Airborne Hyperspectral Imagery for Detection of European Corn Borer Infestation in Iowa Corn Plots

Carroll

Glaser

Hellmich

et al. 2008

Journal of Economic Entomology

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Section: Discussionmentioning

confidence: 91%

Use of Spectral Vegetation Indices Derived from Airborne Hyperspectral Imagery for Detection of European Corn Borer Infestation in Iowa Corn Plots

Carroll

Glaser

Hellmich

et al. 2008

Journal of Economic Entomology

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“…This research was conducted on corn plots of same phenology, and were they managed to minimize the physiological response of the plant to other potential stressors, such as nutrient deÞciency and weeds. We acknowledge that use of hyperspectral imagery to detect plant stress caused by larval O. nubilalis in larger cornÞelds may be more difÞcult given the inherent variability of production sized Þelds especially if there are in-Þeld differences in soil type (Chance 1977, Curran 1985. However, to our knowledge, high spectral and spatial resolution airborne hyperspectral imagery has not been previously used to detect plant stress caused by insect feeding in production corn Þelds.…”

Section: Discussionmentioning

confidence: 99%

Use of Spectral Vegetation Indices Derived from Airborne Hyperspectral Imagery for Detection of European Corn Borer Infestation in Iowa Corn Plots

Carroll

Glaser

Hellmich

et al. 2008

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Eleven spectral vegetation indices that emphasize foliar plant pigments were calculated using airborne hyperspectral imagery and evaluated in 2004 and 2005 for their ability to detect experimental plots of corn manually inoculated with Ostrinia nubilalis (Hübner) neonate larvae. Manual inoculations were timed to simulate infestation of corn, Zea mays L., by first and second flights of adult O. nubilalis. The ability of spectral vegetation indices to detect O. nubilalis-inoculated plots improved as the growing season progressed, with multiple spectral vegetation indices able to identify infested plots in late August and early September. Our findings also indicate that for detecting O. nubilalis-related plant stress in corn, spectral vegetation indices targeting carotenoid and anthocyanin pigments are not as effective as those targeting chlorophyll. Analysis of image data suggests that feeding and stem boring by O. nubilalis larvae may increase the rate of plant senescence causing detectable differences in plant biomass and vigor when compared with control plots. Further, we identified an approximate time frame of 5-6 wk postinoculation, when spectral differences of manually inoculated "second" generation O. nubilalis plots seem to peak.

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“…These models show that plant canopies selectively transmit radiant flux over different spectral regions. Chance (1977), using the model of Suits (1972) for a dense wheat cover, found wheat canopy reflectance to be independent of soil background in the visible spectral region and dependent in the near infrared.…”

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confidence: 99%

Soil‐Dependent Spectral Response in a Developing Plant Canopy¹

Huete

1987

Agronomy Journal

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A major problem in the use of remote sensing techniques to assess plant biomass and condition over incomplete canopies concerns the soil background contribution toward measured spectral response. An understanding of this soil signal is essential to better relate canopy spectra with plant properties. An interactive, plant‐soil radiant flux model was developed to separate spectral variations associated with soil background from those attributable to vegetation. Field measured spectra taken over n developing cotton (Gossypium hirsutum L.) canopy with four soil types (Cumulic Cryoboroll, Typic Torrifluvent, Ustollic Haplargid, and Typic Calciorthid) alternately inserted underneath were decomposed into soil and vegetation spectra by utilizing the model in a principal component analysis. The soil component included all radiation penetrating the canopy and interacting with the underlying soil. The vegetation component represented all radiation reflected directly from the plant cover with no soil interaction. The soil component was found to resemble the spectral response of green vegetation due to the scattering and transmittance properties of the overlying plant canopy. Results show how the soil signal mixes into various vegetation indices inhibiting reliable vegetation discrimination. The potential improvements in vegetation analysis that can result from filtering soil background response from plant‐canopy spectra are also discussed.

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Applications of suits spectral model to wheat

Cited by 8 publications

References 3 publications

Use of Spectral Vegetation Indices Derived from Airborne Hyperspectral Imagery for Detection of European Corn Borer Infestation in Iowa Corn Plots

Use of Spectral Vegetation Indices Derived from Airborne Hyperspectral Imagery for Detection of European Corn Borer Infestation in Iowa Corn Plots

Use of Spectral Vegetation Indices Derived from Airborne Hyperspectral Imagery for Detection of European Corn Borer Infestation in Iowa Corn Plots

Soil‐Dependent Spectral Response in a Developing Plant Canopy¹

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Applications of suits spectral model to wheat

Cited by 8 publications

References 3 publications

Use of Spectral Vegetation Indices Derived from Airborne Hyperspectral Imagery for Detection of European Corn Borer Infestation in Iowa Corn Plots

Use of Spectral Vegetation Indices Derived from Airborne Hyperspectral Imagery for Detection of European Corn Borer Infestation in Iowa Corn Plots

Use of Spectral Vegetation Indices Derived from Airborne Hyperspectral Imagery for Detection of European Corn Borer Infestation in Iowa Corn Plots

Soil‐Dependent Spectral Response in a Developing Plant Canopy1

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Soil‐Dependent Spectral Response in a Developing Plant Canopy¹