Solar Induced Fluorescence and Reflectance Sensing Techniques for Monitoring Nitrogen Utilization in Corn
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Date
2007-06-18
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Citation of Original Publication
L. Corp, E. Middleton, C. Daughtry and P. Campbell, "Solar Induced Fluorescence and Reflectance Sensing Techniques for Monitoring Nitrogen Utilization in Corn," 2006 IEEE International Symposium on Geoscience and Remote Sensing, Denver, CO, USA, 2006, pp. 2267-2270, doi: 10.1109/IGARSS.2006.586.
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract
Remote sensing systems using either passive reflectance (R) or actively induced fluorescence (F) have long been explored as a means to monitor species composition and vegetative productivity. Passive F techniques using the Fraunhofer line depth (FLD) principle to isolate solar induced F (SIF) from the high resolution R continuum have also been suggested for the large-scale remote assessment of vegetation. The FLD principle was applied to both canopy R spectra and AISA multi-spectral imagery to discriminate the relatively weak in situ vegetation F in-fill of the telluric O₂ bands located at 688 nm and 760 nm. The magnitudes of SIF retrieved from R ranged from 7 to 36 mW/m²/nm/sr and the ratio of the two spectral bands successfully discriminated the four N treatment levels. In addition, a number of R indices including but not limited to the physiological reflectance index (PRI), R₅₅₀ /R₅₁₅ and R₇₅₀ /R₈₀₀ were calculated from the AISA aircraft imagery and the high-resolution canopy R spectra. These indexes were then evaluated against georeferenced ground measurements of leaf area index (LAI), pigment contents, grain yields, and light use efficiency (LUE). A number of significant relationships were evident in both R and SIF indices to the biophysical changes in corn induced by N application rates. From this investigation we conclude that valuable SIF information can be extracted from high-resolution canopy R data and indices calculated from both data types can supply useful information for modeling N use for carbon sequestration by vegetation.