Author
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Daughtry, Craig |
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Walthall, Charles |
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Kim, Moon |
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McMurtrey Iii, James |
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Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal Publication Acceptance Date: 3/23/2000 Publication Date: 5/12/2000 Citation: N/A Interpretive Summary: Nitrogen (N) is an essential element for plant growth and is frequently the major limiting nutrient in most agricultural soils. Profitable corn production requires large quantities of N. Farmers must balance the competing goals of supplying adequate N for their crops against minimizing N losses to the environment. Soil testing, plant tissue analysis, and chlorophyll meters require many samples to characterize the spatial variability of N in large fields. Remote sensing techniques have the potential to evaluate the N status of many plants quickly. Our objective was to propose a strategy for detecting leaf chlorophyll status of plants using remotely sensed data. Field corn was supplied with 8 levels of N to establish a wide range of leaf chlorophyll levels. Reflectance and transmittance spectra of upper fully expanded leaves were acquired over the 400-1000 nm wavelength range. Crop canopy reflectance was simulated for a wide range conditions. Differences in leaf spectra were observed near 550 nm, 715 nm, and greater than 750 nm. Variations in background reflectance and LAI confounded detection of the relatively subtle differences in canopy reflectance due to changes in leaf chlorophyll concentration. Some spectral vegetation indices minimized contributions of background reflectance, while others responded to both leaf chlorophyll concentrations and background reflectance. Pairs of the spectral vegetation indices plotted together produced isolines of leaf chlorophyll concentrations. This approach holds promise for characterizing leaf chlorophyll concentrations as a management decision aid without the problem of confounding due to variations in background reflectance and leaf area index. Technical Abstract: Farmers must balance the competing goals of supplying adequate Nitrogen (N) for their crops against minimizing N losses to the environment. Remote sensing techniques have the potential to evaluate the N status of many plants quickly. Our objectives were to simulate canopy reflectance using a radiative transfer model, and propose a strategy for detecting leaf chlorophyll status of plants using remotely sensed data. Field-grown corn (Zea mays L) was supplied with 8 levels of N to establish a wide range of leaf chlorophyll levels. Reflectance and transmittance spectra of upper fully expanded leaves were acquired over the 400-1000 nm wavelength range. Crop canopy reflectance was simulated using the SAIL (Scattering by Arbitrarily Inclined Leaves) model for a wide range of background reflectance, leaf area indices (LAI), and leaf chlorophyll concentrations. Broad band differences in leaf spectra were observed near 550 nm, 715 nm, and greater than 750 nm. Variations in background reflectance and LAI confounded detection of the relatively subtle differences in canopy reflectance due to changes in leaf chlorophyll concentration. Pairs of the spectral vegetation indices plotted together produced isolines of leaf chlorophyll concentrations. Leaf chlorophyll concentration were linearly related to the slopes of these isolines. A limited test with measured canopy data confirmed these results. This approach holds promise for characterizing leaf chlorophyll concentrations as a management decision aid without the problem of confounding due to variations in background reflectance and leaf area index. |
