Farmers must balance the competing goals of supplying adequate N for their crops while minimizing N losses to the environment. To characterize the spatial variability of N over large fields, traditional methods (soil testing, plant tissue analysis, and chlorophyll meters) require many point samples. Because of the close link between leaf chlorophyll and leaf N concentration, remote sensing techniques have the potential to evaluate the N variability over large fields quickly. Our objectives were to (1) select wavelengths sensitive to leaf chlorophyll concentration, (2) simulate canopy reflectance using a radiative transfer model, and (3) propose a strategy for detecting leaf chlorophyll status of plants using remotely sensed data. A wide range of leaf chlorophyll levels was established in field-grown corn ( Zea mays L.) with the application of 8 N levels: 0%, 12.5%, 25%, 50%, 75%, 100%, 125%, and 150% of the recommended rate. Reflectance and transmittance spectra of fully expanded upper leaves were acquired over the 400-nm to 1,000-nm wavelength range shortly after anthesis with a spectroradiometer and integrating sphere. Broad-band differences in leaf spectra were observed near 550 nm, 715 nm, and >750 nm. Crop canopy reflectance was simulated using the SAIL (Scattering by Arbitrarily Inclined Leaves) canopy reflectance model for a wide range of background reflectances, leaf area indices (LAI), and leaf chlorophyll concentrations. Variations in background reflectance and LAI confounded the detection of the relatively subtle differences in canopy reflectance due to changes in leaf chlorophyll concentration. Spectral vegetation indices that combined near-infrared reflectance and red reflectance (e.g., OSAVI and NIR/Red) minimized contributions of background reflectance, while spectral vegetation indices that combined reflectances of near-infrared and other visible bands (MCARI and NIR/Green) were responsive to both leaf chlorophyll concentrations and background reflectance. Pairs of these spectral vegetation indices plotted together produced isolines of leaf chlorophyll concentrations. The slopes of these isolines were linearly related to leaf chlorophyll concentration. A limited test with measured canopy reflectance and leaf chlorophyll data confirmed these results. The characterization of leaf chlorophyll concentrations at the field scale without the confounding problem of background reflectance and LAI variability holds promise as a valuable aid for decision making in managing N applications.
Remote Sensing of Environment – Elsevier
Published: Nov 1, 2000
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera